The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.
CFNAs are present in blood products. Immunoregulatory properties of CFNA are shown in vitro, providing new insights on biologically active components of blood products besides those for intended therapeutic use.
Transfusion-transmitted infections remain a permanent threat in medicine. It keeps the burden of the past, marked by serious infections transmitted by transfusion, and is constantly threatened by emerging viruses. The global rise of immunosuppression among patients undergoing frequent transfusions exacerbates this problem. Over the past decade, criteria for donor selection have become increasingly more stringent. Although routine nucleic acid testing (NAT) for virus-specific detection has become more sensitive, these safety measures are only valuable for a limited number of select viruses. The scientific approach to this is however changing, with the goal of trying to identify infectious agents in donor units as early as possible to mitigate the risk of a clinically relevant infection. To this end, and in addition to an epidemiological surveillance of the general population, researchers are adopting new methods to discover emerging infectious agents, while simultaneously screening for an extended number of viruses in donors. Next-generation sequencing (NGS) offers the opportunity to explore the entire viral landscape in blood donors, the so-called metagenomics, to investigate severe transfusion reactions of unknown etiology. In the not too distant future, one could imagine this platform being used for routine testing of donated blood products.
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